Identification of health behaviour clusters among people at high risk of stroke: A latent class profile analysis

Resourcefulness Among Initial Ischemic Stroke Patients: A Longitudinal Study of 12 Months

Objective

To explore distinct longitudinal trajectories of resourcefulness among initial ischemic stroke patients from diagnosis to 12 months, and to identify whether sociodemographic factors, disease-related factors, self-efficacy, family function, and social support can predict patterns in the trajectories of resourcefulness.

Methods

A prospective longitudinal study was conducted. Initial ischemic stroke patients who met inclusion and exclusion criteria were followed up when still in hospital (Preparing for discharge, Baseline, T1), at 1 month (T2), at 3 months (T3), at 6 months (T4), at 9 months (T5) and 12 months (T6) (±1 week) after discharge. General information, National Institute of Health Stroke Scale (NIHSS), Modified Rankin Scale (mRS), General Self-Efficacy Scale (GSES), General Family Functioning Subscale (FAD-GF), and Social Support Rate Scale (SSRS) were used in T1. The Resourcefulness Scale© was evaluated at 6 time points. Growth mixture modeling was used to identify trajectory patterns of resourcefulness. Logistic regression was used to identify predictors of resourcefulness trajectories.

Results

Three longitudinal trajectories of resourcefulness were identified and named as the high-stable class (38.9%, n=71), fluctuation class (41.2%, n=75), and low-stable class (19.9%, n=36), respectively. Dwelling areas (x2=6.805, P=0.009), education (x2=44.865, P=0.000), monthly income (x2=13.063, P=0.001), NIHSS scores (x2=44.730, P=0.000), mRS scores (x2=51.788, P=0.000), Hcy (x2=9.345, P=0.002), GSES (x2=56.933, P=0.000), FAD-GF (x2=41.305, P=0.000) and SSRS (x2=52.373, P=0.000) were found to be statistically significant for distinguishing between different resourcefulness trajectory patterns. Lower education (OR=0.404), higher NIHSS(OR=6.672) scores, and higher mRS(OR=21.418) scores were found to be risk factors for lower resourcefulness, whereas higher education(OR=0.404), GSES(OR=0.276), FAD-GF(OR=0.344), and SSRS(OR=0.358) scores were identified as protective factors enhancing resourcefulness.

Conclusion

This study obtained three patterns of trajectories and identified their predictive factors in initial ischemic stroke. The findings will assist health care professionals in identifying subgroups of patients and when they may be at risk of low resourcefulness and provide timely targeted intervention to promote resourcefulness.

Is sleep quality a moderated mediator between perceived stress and depression among stroke patients?

Background

Sleep quality can offer new insights into addressing depression among stroke patients. However, the current understanding of the mechanism by which sleep quality reduces depression is not clear in existing research.

Objectives

This study aimed to identify the relationships and mechanisms among perceived stress, sleep quality, social support, and depression in stroke patients.

Methods

A multicenter cross-sectional study was conducted from January to May 2023. Cluster random sampling was used to recruit 500 stroke patients from five hospitals in Henan Province, China. The Chinese Perceived Stress Scale (CPSS), Pittsburgh Sleep Quality Index (PSQI), Social Support Rating Scale (SSRS), and Hamilton Depression Scale (HAMD-24) were employed to assess perceived stress, sleep quality, social support, and depression, respectively. Data were analyzed using descriptive analysis, Pearson's correlation analysis, and moderated mediation analysis. The study adhered to the STROBE checklist for reporting.

Results

Out of 500 participants, 471 completed the survey (94.2%). After controlling for sex and age, mediation analysis revealed that poor sleep quality partially mediated the relationship between perceived stress and depression (β = 0.184, 95% CI: 0.110, 0.359). Additionally, social support played a moderating role in the mediation model.

Conclusion

This study explained the moderated mediation of sleep quality and social support between perceived stress and depression. It provided a theoretical basis for the development of a sleep quality intervention program for reducing depression among stroke patients.

Effects of a digital learning platform on health behaviours in stroke patients from baseline to 6 months after discharge: a randomized controlled trial

AIMS

Improving the health behaviour can help prevent stroke recurrence. The existing health education interventions require more human resource. There is a lack of constructing a low-cost, highly universal, and easy-to-use stroke secondary prevention platform based on the existing medical resources.

METHODS AND RESULTS

This was a randomized controlled trial to test the effects of a digital learning platform on the health knowledge, beliefs, and behaviours of stroke patients from baseline to 6 months after discharge. The control group received routine health education while the intervention group received health belief education during hospitalization and used a digital learning platform for 6 months after discharge. The health knowledge was assessed by The Stroke Health Knowledge Questionnaire, health beliefs by The Short Form Health Belief Model Scale for Stroke Patients, and health behaviours by the Stroke Health Behavior Scale. A total of 90 patients were included: 45 each in the intervention group and the control group, of whom 38 and 37 completed the study, respectively. At 6 months after discharge, (1) the health knowledge score of the intervention group was insignificantly higher than that of the control group, (2) the health belief score of the intervention group was significantly higher than that of the control group, and (3) the intervention group had higher health behaviour scores especially in physical activity than that of the control group. Other health behaviour dimensions have time effect, but not significant.

CONCLUSIONS

The digital learning platform can improve health behaviours of stroke patients 6 months after discharge, especially in physical activity.

REGISTRATION

ChiCTR1800019936.

Patterns of health-promoting behaviors and associated factors in family caregivers of people receiving cancer treatment: A latent class profile analysis

OBJECTIVE

Family caregivers tend to neglect their health while prioritizing the needs of their care recipients. Identifying subgroups of caregivers based on the patterns of health-promoting behaviors (HPBs) may help develop tailored interventions for them, yet little is known. The purpose of this study was: (1) to identify latent classes with distinct patterns of HPBs in family caregivers of people with cancer; and (2) to investigate factors associated with the latent class membership.

METHODS

We performed a cross-sectional data analysis using the baseline dataset from a longitudinal survey study that assessed HPBs of family caregivers of individuals who received cancer treatment at a national research hospital (N = 124). Latent class profile analysis was conducted to identify latent classes based on the subdomains of the Health-Promoting Lifestyle Profile II, followed by multinomial logistic regression analysis to investigate factors associated with the latent class membership.

RESULTS

Three latent classes were identified: a high level of HPB (Class 1, 25.8%); a moderate level of HPB (Class 2, 53.2%); and a low level of HPB (Class 3, 21.0%) of HPBs. Controlling for caregiver age and sex, caregiver burden due to lack of family support, perceived stress, self-efficacy and body mass index were factors associated with the latent class membership.

CONCLUSIONS

HPBs of our caregiver sample appeared in relatively stable patterns at different levels. Higher caregiver burden and perceived stress and lower self-efficacy were associated with the lower practice of HPBs overall. Our findings may serve as a reference for screening caregivers who need support and developing person-centered interventions.

Use of latent profile analysis and k-means clustering to identify student anxiety profiles

BACKGROUND

Anxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions.

METHODS

A cross-sectional study was conducted with 9738 students in Yingshan County. Background characteristics were collected and Mental Health Test (MHT) were completed. Latent profile analysis (LPA) was applied to define student anxiety profiles, and then the analysis was repeated using k-means clustering.

RESULTS

LPA yielded 3 profiles: the low-risk, mild-risk and high-risk groups, which comprised 29.5, 38.1 and 32.4% of the sample, respectively. Repeating the analysis using k-means clustering resulted in similar groupings. Most students in a particular k-means cluster were primarily in a single LPA-derived student profile. The multinomial ordinal logistic regression results showed that the high-risk group was more likely to be female, junior, and introverted, to live in a town, to have lower or average academic performance, to have heavy or average academic pressure, and to be in schools that have never or occasionally have organized mental health education activities.

CONCLUSIONS

The findings suggest that students with anxiety symptoms may be categorized into distinct profiles that are amenable to varying strategies for coordinated interventions.

Effects of Self-Management Intervention Programs Based on the Health Belief Model and Planned Behavior Theory on Self-Management Behavior and Quality of Life in Middle-Aged Stroke Patients

Objectives

To study the effect of self-management intervention programs based on the health belief model and planned behavior theory on self-management behavior and quality of life in middle-aged stroke patients. Most of the intervention studies on the self-management of middle-aged stroke patients focus on traditional Chinese medicine nursing and continuous nursing, lacking theoretical support. In particular, there is a lack of interventions based on the integration of two or more theories.

Method

The middle-aged stroke patients were divided into the control group and the intervention group according to the disease area. A total of 70 patients were included, and 35 patients were included in the control group and the intervention group, respectively. The control group received routine neurological treatment and health education during hospitalization and continued to receive routine health education for 3 months after discharge. On this basis, the intervention group received an intervention program based on an integrated model of health beliefs and planned behavior theory, including 3 health education sessions during hospitalization and 3 months of postdischarge health education. A self-administered stroke general information questionnaire was used to collect basic information on patients' age, gender, and comorbidities. The Stroke Self-Management Behavior Rating Scale and Stroke-Specific Quality-of-Life Scale (SS-QOL) were used to evaluate the management behavior and quality of life of the patients in both groups before and after the intervention.

Results

Before the intervention, there was no statistically significant difference between the two groups in terms of self-management score, quality of life total score, and scores of each dimension (P > 0.05). At different periods after the intervention, the total score of self-management, total score of quality of life, and scores of each dimension were significantly higher in both groups than before the intervention (P < 0.05). In particular, the self-management and quality of life scores of the intervention group were higher than those of the control group at 1 and 3 months after the intervention (P < 0.05).

Conclusion

The self-management intervention scheme based on the integrated model of health belief and planned behavior theory is beneficial to improve the self-management ability and quality of life of stroke patients. It provides basis for clinical nurses to further improve the self-management ability and quality of life of stroke patients. Our findings may also serve as a reference for caregivers in other countries to improve the self-management and quality of life of stroke patients.

Can Goal-Based Health Management Improve the Health Knowledge, Health Belief and Health Behavior in People at High Risk of Stroke? A Non-Randomized Controlled Trial

PURPOSE

To examine the effectiveness of goal-based health management on health knowledge, health belief and health behavior in people at high risk of stroke.

PATIENTS AND METHODS

This is a two-armed and non-randomized controlled trial from May 31, 2020 to December 31, 2020. Participants were arranged to receive regular health care (n=126) or goal-based health management (n=126). The generalized estimation equation was used to analyze the changes in the participants' health knowledge, health belief and health behavior at baseline (T0), 1-month (T1), 3-month (T3), and 6-month (T6).

RESULTS

A total of 243 participants completed the 6-month study, of which 123 were in the control group and 120 were in the intervention group. After receiving goal-based health management, participants' health behavior (β=27.154, p<0.001), health knowledge (β=10.504, p<0.001) and health belief (β=49.057, p<0.001) scores increased significantly.

CONCLUSION

Goal-based health management can effectively improve the health knowledge, health belief and health behavior of people at high risk of stroke. We recommend that nurses can provide goal-based health management for people at high risk of stroke with support from hospitals and family members of people at high risk of stroke.